Abstract: Intelligent water conservancy is an important industry and field of national key information infrastructure.The research on network security situation assessment technology provides powerful support for data protection and network security construction of smart water conservancy.This paper proposes a smart water conservancy situation assessment method based on improved D-S evidence theory,in response to the characteristics of smart water conservancy network models and the problems of insufficient objectivity and large evidence conflicts in network security situation assessment models based on a single D-S evidence theory.Firstly,in the face of massive water conservancy data,deep autoencoders are used to learn features and filter and reduce dimensionality of the data.Then,the processed data is handed over to a deep neural network for binary and multi classification calculations,and the results are fused to obtain the basic probability allocation function value as input for D-S evidence theory.Finally,the fusion rule of D-S evidence theory is used to obtain the final network security situation assessment result.Experimental results show that,compared to traditional situational assessment models,our method can maintain high accuracy while improving objectivity.

Key words: Intelligent water conservancy, Network security situation awareness, D-S theory of evidence, Deep autoencoder, Deep neural networks